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...ity. I want to estimate the confidence interval of the variance coefficient and so I should use a bootstrap simulation. The pedigree file has 1386 subjects so I create a kinship matrix [1386*1386].This is the code of R I use: kfit2 <- lmekin(IT~1+AGE +(1|ID), dati1, varlist=list(kmat))kfit2kfit2$vcoef library(boot) var <- function(formula,data,indices,varlist) { d <- data[indices,] # allows boot to select sample kfit2 <- lmekin(formula, data=d, varlist=list(kmat)) return(kfit2$vcoef)} # bootstrapping with 1000 replications results <- boot(data=dati1, statistic=var, R=1000,...

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

...for (i in 1:50){ data2<-simulWeib(25000,lambda=0.0001,rho=2,beta1=0.33,beta2=5,beta3=0.25,beta4=0,rateC=0.0000000001, sigma = 0.25) data2$id<-as.factor(data2$id) fit.cox2<-coxme(Surv(time,status) ~ x1 + x2 + x3 + (1 | id), data=data2) sd.2[i]<-sqrt(as.numeric(fit.cox2$vcoef)) print(i) } print("model 2 done") ### Same as previous example, but patients are grouped sd.10<-rep(0,50) for (i in 1:50){ data10<-simulWeib.group(25000,lambda=0.0001,rho=2,beta1=0.33,beta2=5,beta3=0.25,beta4=0,rateC=0.0000000001, sigma = 0.25, M=40) data1...